{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5a312642",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0edea92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Series\n",
    "s1 = pd.Series(['a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad0abdca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f898bbad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ceef2888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a1d9eab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "k1    v1\n",
       "k2    v2\n",
       "k3    v3\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({'k1':'v1','k2':'v2','k3':'v3'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c66a86dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = pd.Series(['a','b','c'],index=['10','11','12'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80008bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    a\n",
       "11    b\n",
       "12    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6788bae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2['10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6abe1e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = pd.Series([10,20,30,40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c631fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c9c3f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9fe43b84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dbb722ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20, 30, 40], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb87d44b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=4, step=1)\n"
     ]
    }
   ],
   "source": [
    "print(s3.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ab5ba816",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({\"name\":[\"张三\",\"李四\",\"王五\"],\"age\":[20,22,21],\"gender\":[\"男\",\"女\",\"男\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5dbeb5aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4dbc3bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>20</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  age gender\n",
       "0   张三   20      男\n",
       "1   李四   22      女\n",
       "2   王五   21      男"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cf51c237",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  name  age gender\n",
      "0   张三   20      男\n",
      "1   李四   22      女\n",
      "2   王五   21      男\n"
     ]
    }
   ],
   "source": [
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ff0f76b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1[['name','age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1834578e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df1['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "861e296f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02eb863a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6ff37817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    张三\n",
       "1    李四\n",
       "2    王五\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "69792b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df1['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d3eae00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20\n",
       "1    22\n",
       "2    21\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0372bbfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  age\n",
       "0   张三   20\n",
       "1   李四   22\n",
       "2   王五   21"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"name\":df3,\"age\":df4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4745e905",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuDF = pd.read_csv(\"./data/students.txt\",names=['id','name','age','gender','clazz'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "da2224e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz\n",
       "0  1500100001  施笑槐   22      女  文科六班\n",
       "1  1500100002  吕金鹏   24      男  文科六班\n",
       "2  1500100003  单乐蕊   22      女  理科六班\n",
       "3  1500100004  葛德曜   24      男  理科三班\n",
       "4  1500100005  宣谷芹   22      女  理科五班"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "81725d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1500100996</td>\n",
       "      <td>厉运凡</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1500100997</td>\n",
       "      <td>陶敬曦</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1500100998</td>\n",
       "      <td>容昆宇</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "      <td>理科四班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1500100999</td>\n",
       "      <td>钟绮晴</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科五班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1500101000</td>\n",
       "      <td>符瑞渊</td>\n",
       "      <td>23</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id name  age gender clazz\n",
       "995  1500100996  厉运凡   24      男  文科三班\n",
       "996  1500100997  陶敬曦   21      男  理科六班\n",
       "997  1500100998  容昆宇   22      男  理科四班\n",
       "998  1500100999  钟绮晴   23      女  文科五班\n",
       "999  1500101000  符瑞渊   23      男  理科六班"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "05f19714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1500100006</td>\n",
       "      <td>边昂雄</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科二班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1500100007</td>\n",
       "      <td>尚孤风</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1500100008</td>\n",
       "      <td>符半双</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1500100009</td>\n",
       "      <td>沈德昌</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科一班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1500100010</td>\n",
       "      <td>羿彦昌</td>\n",
       "      <td>23</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz\n",
       "0  1500100001  施笑槐   22      女  文科六班\n",
       "1  1500100002  吕金鹏   24      男  文科六班\n",
       "2  1500100003  单乐蕊   22      女  理科六班\n",
       "3  1500100004  葛德曜   24      男  理科三班\n",
       "4  1500100005  宣谷芹   22      女  理科五班\n",
       "5  1500100006  边昂雄   21      男  理科二班\n",
       "6  1500100007  尚孤风   23      女  文科六班\n",
       "7  1500100008  符半双   22      女  理科六班\n",
       "8  1500100009  沈德昌   21      男  理科一班\n",
       "9  1500100010  羿彦昌   23      男  理科六班"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2f0805c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>24</td>\n",
       "      <td>厉运凡</td>\n",
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       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>21</td>\n",
       "      <td>陶敬曦</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>22</td>\n",
       "      <td>容昆宇</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>23</td>\n",
       "      <td>钟绮晴</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>23</td>\n",
       "      <td>符瑞渊</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
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      "text/plain": [
       "     age name\n",
       "0     22  施笑槐\n",
       "1     24  吕金鹏\n",
       "2     22  单乐蕊\n",
       "3     24  葛德曜\n",
       "4     22  宣谷芹\n",
       "..   ...  ...\n",
       "995   24  厉运凡\n",
       "996   21  陶敬曦\n",
       "997   22  容昆宇\n",
       "998   23  钟绮晴\n",
       "999   23  符瑞渊\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF[['age','name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f779339b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22\n",
       "1      24\n",
       "2      22\n",
       "3      24\n",
       "4      22\n",
       "       ..\n",
       "995    24\n",
       "996    21\n",
       "997    22\n",
       "998    23\n",
       "999    23\n",
       "Name: age, Length: 1000, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e48a39e3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>12</th>\n",
       "      <td>1500100013</td>\n",
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       "      <td>男</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1500100019</td>\n",
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       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
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       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1500100996</td>\n",
       "      <td>厉运凡</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科三班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id name  age gender clazz\n",
       "1    1500100002  吕金鹏   24      男  文科六班\n",
       "3    1500100004  葛德曜   24      男  理科三班\n",
       "12   1500100013  逯君昊   24      男  文科二班\n",
       "18   1500100019  娄曦之   24      男  理科三班\n",
       "27   1500100028  幸浩邈   24      男  理科五班\n",
       "..          ...  ...  ...    ...   ...\n",
       "988  1500100989  柏盼香   24      女  理科六班\n",
       "991  1500100992  莫运盛   24      男  理科六班\n",
       "993  1500100994  相凌青   24      女  理科四班\n",
       "994  1500100995  寿芷卉   24      女  理科五班\n",
       "995  1500100996  厉运凡   24      男  文科三班\n",
       "\n",
       "[260 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF[stuDF['age']>23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c5f64d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "clazz_cnt = stuDF.groupby('clazz')['id'].count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7b407b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>11</th>\n",
       "      <td>理科四班</td>\n",
       "      <td>91</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   clazz   id\n",
       "0   文科一班   72\n",
       "1   文科三班   94\n",
       "2   文科二班   87\n",
       "3   文科五班   84\n",
       "4   文科六班  104\n",
       "5   文科四班   81\n",
       "6   理科一班   78\n",
       "7   理科三班   68\n",
       "8   理科二班   79\n",
       "9   理科五班   70\n",
       "10  理科六班   92\n",
       "11  理科四班   91"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_cnt.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3ff44bfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "clazz_cnt.rename(columns={\"id\":\"cnt\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a69e38b1",
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "   clazz  cnt\n",
       "0   文科一班   72\n",
       "1   文科三班   94\n",
       "2   文科二班   87\n",
       "3   文科五班   84\n",
       "4   文科六班  104\n",
       "5   文科四班   81\n",
       "6   理科一班   78\n",
       "7   理科三班   68\n",
       "8   理科二班   79\n",
       "9   理科五班   70\n",
       "10  理科六班   92\n",
       "11  理科四班   91"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c9c0e1b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>文科六班</td>\n",
       "      <td>104</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>理科六班</td>\n",
       "      <td>92</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clazz  cnt\n",
       "4   文科六班  104\n",
       "1   文科三班   94\n",
       "10  理科六班   92"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "clazz_cnt.sort_values('cnt',ascending=False)[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "38d9f0d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>文科六班</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>文科四班</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>理科一班</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>理科三班</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>理科二班</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clazz  cnt\n",
       "4  文科六班  104\n",
       "5  文科四班   81\n",
       "6  理科一班   78\n",
       "7  理科三班   68\n",
       "8  理科二班   79"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_cnt.loc[4:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "95210f99",
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   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
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       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz\n",
       "0  1500100001  施笑槐   22      女  文科六班\n",
       "1  1500100002  吕金鹏   24      男  文科六班\n",
       "2  1500100003  单乐蕊   22      女  理科六班\n",
       "3  1500100004  葛德曜   24      男  理科三班\n",
       "4  1500100005  宣谷芹   22      女  理科五班"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c0d1bd48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      1000 non-null   int64 \n",
      " 1   name    1000 non-null   object\n",
      " 2   age     1000 non-null   int64 \n",
      " 3   gender  1000 non-null   object\n",
      " 4   clazz   1000 non-null   object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 39.2+ KB\n"
     ]
    }
   ],
   "source": [
    "stuDF.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "75fbd066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.500101e+09</td>\n",
       "      <td>22.521000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.888194e+02</td>\n",
       "      <td>1.113013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.500100e+09</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.500100e+09</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500101e+09</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.500101e+09</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.500101e+09</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                 id          age\n",
       "count  1.000000e+03  1000.000000\n",
       "mean   1.500101e+09    22.521000\n",
       "std    2.888194e+02     1.113013\n",
       "min    1.500100e+09    21.000000\n",
       "25%    1.500100e+09    22.000000\n",
       "50%    1.500101e+09    22.000000\n",
       "75%    1.500101e+09    24.000000\n",
       "max    1.500101e+09    24.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "393bccf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0.021497</td>\n",
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       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.021497</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "           id       age\n",
       "id   1.000000  0.021497\n",
       "age  0.021497  1.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "50baf2e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "scoreDF = pd.read_csv(\"./data/score.txt\",names=['student_id','subject_id','score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "055e6b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000001</td>\n",
       "      <td>98</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000002</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000003</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000004</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000005</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_id  subject_id  score\n",
       "0  1500100001     1000001     98\n",
       "1  1500100001     1000002      5\n",
       "2  1500100001     1000003    137\n",
       "3  1500100001     1000004     29\n",
       "4  1500100001     1000005     85"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoreDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2d028050",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz\n",
       "0  1500100001  施笑槐   22      女  文科六班\n",
       "1  1500100002  吕金鹏   24      男  文科六班\n",
       "2  1500100003  单乐蕊   22      女  理科六班\n",
       "3  1500100004  葛德曜   24      男  理科三班\n",
       "4  1500100005  宣谷芹   22      女  理科五班"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1d1d6ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 班级总分前3名\n",
    "# 计算总分\n",
    "sum_scoreDF = scoreDF.groupby('student_id')['score'].agg('sum').reset_index().rename(columns={\"score\":\"sum_score\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e4a38698",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>sum_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_id  sum_score\n",
       "0  1500100001        406\n",
       "1  1500100002        440\n",
       "2  1500100003        359\n",
       "3  1500100004        421\n",
       "4  1500100005        395"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_scoreDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d4d51022",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 关联 merge\n",
    "# how： left right inner\n",
    "stu_sum_scoreDF = stuDF.merge(sum_scoreDF,left_on='id',right_on='student_id',how='left').drop('student_id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6c017335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "      <th>sum_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz  sum_score\n",
       "0  1500100001  施笑槐   22      女  文科六班        406\n",
       "1  1500100002  吕金鹏   24      男  文科六班        440\n",
       "2  1500100003  单乐蕊   22      女  理科六班        359\n",
       "3  1500100004  葛德曜   24      男  理科三班        421\n",
       "4  1500100005  宣谷芹   22      女  理科五班        395"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu_sum_scoreDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8b48970b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'average'，'first'，'min'， 'max'，'dense'\n",
    "stu_sum_scoreDF['first_rank'] = stu_sum_scoreDF.groupby('clazz')['sum_score'].rank(method='first',ascending=False)\n",
    "stu_sum_scoreDF['min_rank'] = stu_sum_scoreDF.groupby('clazz')['sum_score'].rank(method='min',ascending=False)\n",
    "stu_sum_scoreDF['max_rank'] = stu_sum_scoreDF.groupby('clazz')['sum_score'].rank(method='max',ascending=False)\n",
    "stu_sum_scoreDF['dense_rank'] = stu_sum_scoreDF.groupby('clazz')['sum_score'].rank(method='dense',ascending=False)\n",
    "stu_sum_scoreDF['average_rank'] = stu_sum_scoreDF.groupby('clazz')['sum_score'].rank(method='average',ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4ff0fa43",
   "metadata": {},
   "outputs": [],
   "source": [
    "stu_sum_scoreDF = stu_sum_scoreDF.sort_values(['clazz','first_rank'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "31a2e185",
   "metadata": {},
   "outputs": [],
   "source": [
    "clazz_top3 = stu_sum_scoreDF[stu_sum_scoreDF['first_rank']<=3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bc4f1e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "85bd27f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# windows解决中文乱码\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签\n",
    "\n",
    "# mac解决中文乱码\n",
    "# plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']\n",
    "\n",
    "plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f356850b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.title(\"班级总分Top3\")\n",
    "sns.barplot(x=\"clazz\", y=\"sum_score\", hue=\"first_rank\", data=clazz_top3)\n",
    "plt.xlabel(\"班级\")\n",
    "plt.ylabel(\"总成绩\")\n",
    "plt.ylim((450,650))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "97d10781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clazz</th>\n",
       "      <th>gender</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>女</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>男</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>女</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>男</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>文科二班</td>\n",
       "      <td>女</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clazz gender  cnt\n",
       "0  文科一班      女   41\n",
       "1  文科一班      男   31\n",
       "2  文科三班      女   44\n",
       "3  文科三班      男   50\n",
       "4  文科二班      女   38"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每个班级的性别占比\n",
    "clazz_gender_DF = stuDF.groupby([\"clazz\",\"gender\"])['id'].count().reset_index().rename(columns={\"id\":\"cnt\"})\n",
    "clazz_gender_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "741adf35",
   "metadata": {},
   "outputs": [],
   "source": [
    "clazz_gender_labels = clazz_gender_DF['clazz']+\"-\"+clazz_gender_DF['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5b2f86ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.pie(clazz_gender_DF['cnt'],\n",
    "       labels=clazz_gender_labels.values,\n",
    "       autopct='%.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "36c763b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制子图\n",
    "plt.figure(figsize=(16,10))\n",
    "\n",
    "i = 1\n",
    "for clazz in clazz_gender_DF.clazz.unique():\n",
    "    plt.subplot(3,4,i)\n",
    "    plt.title('%s性别人数' % clazz)\n",
    "    clazz1 = clazz_gender_DF[clazz_gender_DF['clazz']== clazz]\n",
    "    plt.pie(clazz1['cnt'],labels=clazz1['gender'],autopct='%.2f%%',colors=[\"#\"+\"\".join([random.choice('0123456789ABCDEF') for i in range(6)]) for j in range(2)])\n",
    "    i+=1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0d2ee132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clazz</th>\n",
       "      <th>gender</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>女</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>男</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clazz gender  cnt\n",
       "0  文科一班      女   41\n",
       "1  文科一班      男   31"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_gender_DF[clazz_gender_DF['clazz']=='文科一班']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d211b0c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# i = 1\n",
    "# for clazz in clazz_gender_DF.clazz.unique():\n",
    "#     plt.subplot(3,4,i)\n",
    "#     plt.title('%s性别人数' % clazz)\n",
    "#     clazz1 = clazz_gender_DF[clazz_gender_DF['clazz']== clazz]\n",
    "#     plt.pie(clazz1['cnt'],labels=clazz1['gender'])\n",
    "#     i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "3195bb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "list1=[]\n",
    "for i in range(6):\n",
    "    list1.append(random.choice('0123456789ABCDEF'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "2a245f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'#C149C8'"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"#\"+\"\".join(list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "19f7d502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'#0146E0'"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"#\"+\"\".join([random.choice('0123456789ABCDEF') for i in range(6)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "a96c4490",
   "metadata": {},
   "outputs": [],
   "source": [
    "list2 = []\n",
    "for j in range(2):\n",
    "    list2.append(\"#\"+\"\".join([random.choice('0123456789ABCDEF') for i in range(6)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "aa429fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#1D84C3', '#1C912C']"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "96541ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#7BF0BB', '#B02693']"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\"#\"+\"\".join([random.choice('0123456789ABCDEF') for i in range(6)]) for j in range(2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "0b77577c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "      <th>last_mod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>2021-12-06 16:41:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>2021-12-06 16:41:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>2021-12-06 16:41:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>25</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "      <td>2021-12-06 16:41:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "      <td>2021-12-06 16:41:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1500100996</td>\n",
       "      <td>厉运凡</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科三班</td>\n",
       "      <td>2021-12-06 16:41:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1500100997</td>\n",
       "      <td>陶敬曦</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>2021-12-06 16:41:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1500100998</td>\n",
       "      <td>容昆宇</td>\n",
       "      <td>223</td>\n",
       "      <td>男</td>\n",
       "      <td>理科四班</td>\n",
       "      <td>2021-12-06 16:49:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1500100999</td>\n",
       "      <td>钟绮晴</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科五班</td>\n",
       "      <td>2021-12-06 16:46:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1500101000</td>\n",
       "      <td>符瑞渊</td>\n",
       "      <td>25</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>2020-12-05 16:46:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id name  age gender clazz            last_mod\n",
       "0    1500100001  施笑槐   22      女  文科六班 2021-12-06 16:41:38\n",
       "1    1500100002  吕金鹏   24      男  文科六班 2021-12-06 16:41:38\n",
       "2    1500100003  单乐蕊   22      女  理科六班 2021-12-06 16:41:38\n",
       "3    1500100004  葛德曜   25      男  理科三班 2021-12-06 16:41:38\n",
       "4    1500100005  宣谷芹   22      女  理科五班 2021-12-06 16:41:38\n",
       "..          ...  ...  ...    ...   ...                 ...\n",
       "995  1500100996  厉运凡   24      男  文科三班 2021-12-06 16:41:38\n",
       "996  1500100997  陶敬曦   21      男  理科六班 2021-12-06 16:41:38\n",
       "997  1500100998  容昆宇  223      男  理科四班 2021-12-06 16:49:04\n",
       "998  1500100999  钟绮晴   22      女  文科五班 2021-12-06 16:46:27\n",
       "999  1500101000  符瑞渊   25      男  理科六班 2020-12-05 16:46:30\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取MySQL中的数据\n",
    "import pymysql\n",
    "conn = pymysql.connect(user='root',passwd='123456',host='master',port=3306,db='student')\n",
    "pd.read_sql(\"select * from student\",con=conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "d03bdc56",
   "metadata": {},
   "outputs": [],
   "source": [
    "stu_sum_scoreDF.to_csv(\"./data/stu_sumscore_DF.csv\",index=None,header=None,encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "abd73ce3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "c:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "c:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "c:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "tmp_df = stu_sum_scoreDF[['id','name','age','gender','clazz','sum_score','first_rank']]\n",
    "\n",
    "tmp_df['sum_score'] = tmp_df.sum_score.astype('str')\n",
    "tmp_df['first_rank'] = tmp_df.first_rank.astype('str')\n",
    "tmp_df['id'] = tmp_df.id.astype('str')\n",
    "tmp_df['age'] = tmp_df.age.astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f5eda152",
   "metadata": {},
   "outputs": [
    {
     "ename": "DatabaseError",
     "evalue": "Execution failed on sql 'SELECT name FROM sqlite_master WHERE type='table' AND name=?;': not all arguments converted during string formatting",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2055\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2056\u001b[1;33m             \u001b[0mcur\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2057\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mcur\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pymysql\\cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, query, args)\u001b[0m\n\u001b[0;32m    145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m         \u001b[0mquery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmogrify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pymysql\\cursors.py\u001b[0m in \u001b[0;36mmogrify\u001b[1;34m(self, query, args)\u001b[0m\n\u001b[0;32m    124\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0margs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m             \u001b[0mquery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquery\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_escape_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: not all arguments converted during string formatting",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mDatabaseError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13624/3536339720.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mcnn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw_connection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# 需要用到 sqlalchemy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtmp_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_sql\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'test2'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcnn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mif_exists\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'replace'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(self, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)\u001b[0m\n\u001b[0;32m   2880\u001b[0m             \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2881\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2882\u001b[1;33m             \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2883\u001b[0m         )\n\u001b[0;32m   2884\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method, engine, **engine_kwargs)\u001b[0m\n\u001b[0;32m    726\u001b[0m         \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    727\u001b[0m         \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 728\u001b[1;33m         \u001b[1;33m**\u001b[0m\u001b[0mengine_kwargs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    729\u001b[0m     )\n\u001b[0;32m    730\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method, **kwargs)\u001b[0m\n\u001b[0;32m   2223\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2224\u001b[0m         )\n\u001b[1;32m-> 2225\u001b[1;33m         \u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2226\u001b[0m         \u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    854\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    855\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 856\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    857\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mif_exists\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"fail\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Table '{self.name}' already exists.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mexists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    838\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    839\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 840\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpd_sql\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    841\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    842\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msql_schema\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mhas_table\u001b[1;34m(self, name, schema)\u001b[0m\n\u001b[0;32m   2234\u001b[0m         \u001b[0mquery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mf\"SELECT name FROM sqlite_master WHERE type='table' AND name={wld};\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2236\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetchall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2237\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2238\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zzk10\\python3.7.9\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2066\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2067\u001b[0m             \u001b[0mex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDatabaseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Execution failed on sql '{args[0]}': {exc}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2068\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mex\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2069\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2070\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDatabaseError\u001b[0m: Execution failed on sql 'SELECT name FROM sqlite_master WHERE type='table' AND name=?;': not all arguments converted during string formatting"
     ]
    }
   ],
   "source": [
    "# from sqlalchemy import create_engine\n",
    "# engine = create_engine(\"mysql+pymysql://root:123456@master:3306/student\")\n",
    "# cnn = engine.raw_connection()\n",
    "# 需要用到 sqlalchemy\n",
    "# tmp_df.to_sql(name='test2',con=cnn,if_exists='replace',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "5e8d101d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000 entries, 307 to 180\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            1000 non-null   int64  \n",
      " 1   name          1000 non-null   object \n",
      " 2   age           1000 non-null   int64  \n",
      " 3   gender        1000 non-null   object \n",
      " 4   clazz         1000 non-null   object \n",
      " 5   sum_score     1000 non-null   int64  \n",
      " 6   first_rank    1000 non-null   float64\n",
      " 7   min_rank      1000 non-null   float64\n",
      " 8   max_rank      1000 non-null   float64\n",
      " 9   dense_rank    1000 non-null   float64\n",
      " 10  average_rank  1000 non-null   float64\n",
      "dtypes: float64(5), int64(3), object(3)\n",
      "memory usage: 126.0+ KB\n"
     ]
    }
   ],
   "source": [
    "stu_sum_scoreDF.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "2baaeacb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "      <th>sum_score</th>\n",
       "      <th>first_rank</th>\n",
       "      <th>min_rank</th>\n",
       "      <th>max_rank</th>\n",
       "      <th>dense_rank</th>\n",
       "      <th>average_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1500100308</td>\n",
       "      <td>黄初夏</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科一班</td>\n",
       "      <td>628</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>1500100875</td>\n",
       "      <td>马向南</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>文科一班</td>\n",
       "      <td>595</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>1500100943</td>\n",
       "      <td>许昌黎</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>文科一班</td>\n",
       "      <td>580</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>1500100871</td>\n",
       "      <td>贝惜梦</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>文科一班</td>\n",
       "      <td>569</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1500100128</td>\n",
       "      <td>巫鸿哲</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科一班</td>\n",
       "      <td>549</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id name  age gender clazz  sum_score  first_rank  min_rank  \\\n",
       "307  1500100308  黄初夏   23      女  文科一班        628         1.0       1.0   \n",
       "874  1500100875  马向南   21      女  文科一班        595         2.0       2.0   \n",
       "942  1500100943  许昌黎   21      男  文科一班        580         3.0       3.0   \n",
       "870  1500100871  贝惜梦   24      女  文科一班        569         4.0       4.0   \n",
       "127  1500100128  巫鸿哲   24      男  文科一班        549         5.0       5.0   \n",
       "\n",
       "     max_rank  dense_rank  average_rank  \n",
       "307       1.0         1.0           1.0  \n",
       "874       2.0         2.0           2.0  \n",
       "942       3.0         3.0           3.0  \n",
       "870       4.0         4.0           4.0  \n",
       "127       5.0         5.0           5.0  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu_sum_scoreDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "e7be5047",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "9353a065",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "6251c493",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = engine.raw_connection()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f72acda",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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